repo_name stringlengths 7 90 | path stringlengths 5 191 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 976 581k | license stringclasses 15
values |
|---|---|---|---|---|---|
saiwing-yeung/scikit-learn | sklearn/tests/test_base.py | 5 | 7693 | # Author: Gael Varoquaux
# License: BSD 3 clause
import sys
import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_false
from sklearn.utils.testing import assert_equal
from sklearn.utils.... | bsd-3-clause |
ishank08/scikit-learn | sklearn/datasets/lfw.py | 15 | 18695 | """Loader for the Labeled Faces in the Wild (LFW) dataset
This dataset is a collection of JPEG pictures of famous people collected
over the internet, all details are available on the official website:
http://vis-www.cs.umass.edu/lfw/
Each picture is centered on a single face. The typical task is called
Face Veri... | bsd-3-clause |
enigmampc/catalyst | tests/pipeline/test_term.py | 1 | 25102 | """
Tests for Term.
"""
from collections import Counter
from itertools import product
from unittest import TestCase
from toolz import assoc
import pandas as pd
from catalyst.assets import Asset
from catalyst.errors import (
DTypeNotSpecified,
InvalidOutputName,
NonWindowSafeInput,
NotDType,
TermIn... | apache-2.0 |
reshama/data-science-from-scratch | code/clustering.py | 60 | 6438 | from __future__ import division
from linear_algebra import squared_distance, vector_mean, distance
import math, random
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
class KMeans:
"""performs k-means clustering"""
def __init__(self, k):
self.k = k # number of clusters
... | unlicense |
iismd17/scikit-learn | sklearn/metrics/pairwise.py | 49 | 44088 | # -*- coding: utf-8 -*-
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Mathieu Blondel <mathieu@mblondel.org>
# Robert Layton <robertlayton@gmail.com>
# Andreas Mueller <amueller@ais.uni-bonn.de>
# Philippe Gervais <philippe.gervais@inria.fr>
# Lars Buitinck ... | bsd-3-clause |
bthirion/nistats | nistats/tests/test_utils.py | 1 | 7186 | #!/usr/bin/env python
import os
import numpy as np
import pandas as pd
import scipy.linalg as spl
from nibabel.tmpdirs import InTemporaryDirectory
from nilearn.datasets.tests import test_utils as tst
from nose import with_setup
from nose.tools import (assert_true,
assert_equal,
... | bsd-3-clause |
schreiberx/sweet | scripts/normal_modes_plot_and_analyse.py | 2 | 4514 | #!/usr/bin/env python2
import sys
import numpy as np
from mpmath import mp
import matplotlib.pyplot as plt
import math
if len(sys.argv) < 2:
print("Execute with [executable] [input]")
sys.exit(1)
filename = sys.argv[1]
print("Loading data from "+filename)
data = np.loadtxt(filename, skiprows=0)
rows,cols = data.... | mit |
lenovor/scikit-learn | sklearn/ensemble/tests/test_base.py | 284 | 1328 | """
Testing for the base module (sklearn.ensemble.base).
"""
# Authors: Gilles Louppe
# License: BSD 3 clause
from numpy.testing import assert_equal
from nose.tools import assert_true
from sklearn.utils.testing import assert_raise_message
from sklearn.datasets import load_iris
from sklearn.ensemble import BaggingCla... | bsd-3-clause |
rohanp/scikit-learn | examples/gaussian_process/plot_gpr_co2.py | 131 | 5705 | """
========================================================
Gaussian process regression (GPR) on Mauna Loa CO2 data.
========================================================
This example is based on Section 5.4.3 of "Gaussian Processes for Machine
Learning" [RW2006]. It illustrates an example of complex kernel engine... | bsd-3-clause |
Storj/storjnet | benchmark/filterupdates_push/quasar_extraprop_plot.py | 1 | 2868 | # -*- coding: utf-8 -*-
import matplotlib.pyplot as plt # pip install matplotlib
import json
x = []
y_success = []
y_redundant = []
y_spam = []
samples = [
json.load(open("benchmark/filterupdates_push/quasar_extraprop_a.json", "r")),
json.load(open("benchmark/filterupdates_push/quasar_extraprop_b.json", "r... | mit |
mhvk/astropy | astropy/visualization/wcsaxes/grid_paths.py | 5 | 4066 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
import numpy as np
from matplotlib.lines import Path
from astropy.coordinates.angle_utilities import angular_separation
# Tolerance for WCS round-tripping, relative to the scale size
ROUND_TRIP_RTOL = 1.
# Tolerance for discontinuities relative to th... | bsd-3-clause |
terkkila/scikit-learn | sklearn/feature_selection/rfe.py | 137 | 17066 | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Vincent Michel <vincent.michel@inria.fr>
# Gilles Louppe <g.louppe@gmail.com>
#
# License: BSD 3 clause
"""Recursive feature elimination for feature ranking"""
import warnings
import numpy as np
from ..utils import check_X_y, safe_sqr
fro... | bsd-3-clause |
philo-zhang/cnn-tracker | show_training_log.py | 1 | 4683 | import os
import re
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from argparse import ArgumentParser
from collections import defaultdict
def _parse(file_path, pattern_iter, pattern_loss, pattern_accuracy):
with open(file_path, 'r') as f:
log = f.read()
iter_list = map(in... | gpl-2.0 |
nmayorov/scikit-learn | examples/cluster/plot_segmentation_toy.py | 91 | 3522 | """
===========================================
Spectral clustering for image segmentation
===========================================
In this example, an image with connected circles is generated and
spectral clustering is used to separate the circles.
In these settings, the :ref:`spectral_clustering` approach solve... | bsd-3-clause |
jreback/pandas | pandas/tests/test_take.py | 3 | 16875 | from datetime import datetime
import re
import numpy as np
import pytest
from pandas._libs import iNaT
import pandas._testing as tm
import pandas.core.algorithms as algos
@pytest.fixture(params=[True, False])
def writeable(request):
return request.param
# Check that take_nd works both with writeable arrays
#... | bsd-3-clause |
ningchi/scikit-learn | sklearn/tests/test_naive_bayes.py | 142 | 17496 | import pickle
from io import BytesIO
import numpy as np
import scipy.sparse
from sklearn.datasets import load_digits, load_iris
from sklearn.cross_validation import cross_val_score, train_test_split
from sklearn.externals.six.moves import zip
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.te... | bsd-3-clause |
wathen/PhD | MHD/FEniCS/FieldSplit/LSC/MUresults/3d.py | 1 | 12582 |
#!/opt/local/bin/python
from dolfin import *
import petsc4py
import sys
petsc4py.init(sys.argv)
from petsc4py import PETSc
# from MatrixOperations import *
import numpy as np
import matplotlib.pylab as plt
import os
import scipy.io
#from PyTrilinos import Epetra, EpetraExt, AztecOO, ML, Amesos
#from scipy2Trilinos i... | mit |
xwolf12/androguard | elsim/elsim/elsim.py | 37 | 16175 | # This file is part of Elsim
#
# Copyright (C) 2012, Anthony Desnos <desnos at t0t0.fr>
# All rights reserved.
#
# Elsim is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published by
# the Free Software Foundation, either version 3 of the License, ... | apache-2.0 |
nmayorov/scikit-learn | sklearn/linear_model/tests/test_theil_sen.py | 58 | 9948 | """
Testing for Theil-Sen module (sklearn.linear_model.theil_sen)
"""
# Author: Florian Wilhelm <florian.wilhelm@gmail.com>
# License: BSD 3 clause
from __future__ import division, print_function, absolute_import
import os
import sys
from contextlib import contextmanager
import numpy as np
from numpy.testing import ... | bsd-3-clause |
ndingwall/scikit-learn | sklearn/neighbors/_kde.py | 7 | 10831 | """
Kernel Density Estimation
-------------------------
"""
# Author: Jake Vanderplas <jakevdp@cs.washington.edu>
import numpy as np
from scipy.special import gammainc
from ..base import BaseEstimator
from ..utils import check_array, check_random_state
from ..utils.validation import _check_sample_weight, check_is_fitt... | bsd-3-clause |
quom/google-cloud-python | monitoring/google/cloud/monitoring/query.py | 7 | 24410 | # Copyright 2016 Google Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | apache-2.0 |
luo66/scikit-learn | examples/cluster/plot_adjusted_for_chance_measures.py | 286 | 4353 | """
==========================================================
Adjustment for chance in clustering performance evaluation
==========================================================
The following plots demonstrate the impact of the number of clusters and
number of samples on various clustering performance evaluation me... | bsd-3-clause |
466152112/scikit-learn | examples/hetero_feature_union.py | 288 | 6236 | """
=============================================
Feature Union with Heterogeneous Data Sources
=============================================
Datasets can often contain components of that require different feature
extraction and processing pipelines. This scenario might occur when:
1. Your dataset consists of hetero... | bsd-3-clause |
prateeksaxena2809/opencog | opencog/embodiment/Monitor/monitor_main.py | 17 | 4138 | #!/usr/bin/env python
#
# Main window for OAC monitor
#
# It is only used for development, you should use 'python oc_workbench.py'
# to start the OpenCog workbench
#
# @author: Zhenhua Cai, czhedu@gmail.com
# @date: 2011-03-30
#
# @note: I borrowed some code from
# http://matplotlib.sourceforge.net/examples/user_... | agpl-3.0 |
niamoto/niamoto-core | niamoto/data_marts/dimensions/base_dimension.py | 2 | 12926 | # coding: utf-8
import io
from datetime import datetime
from sqlalchemy.engine.reflection import Inspector
from geoalchemy2 import Geography, Geometry
import sqlalchemy as sa
import pandas as pd
from niamoto.db import metadata as meta
from niamoto.db.connector import Connector
from niamoto.conf import settings
from ... | gpl-3.0 |
kenshay/ImageScript | ProgramData/SystemFiles/Python/Lib/site-packages/mpl_toolkits/axes_grid1/axes_rgb.py | 6 | 7005 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
import numpy as np
from .axes_divider import make_axes_locatable, Size, locatable_axes_factory
import sys
from .mpl_axes import Axes
def make_rgb_axes(ax, pad=0.01, axes_class=None, add_all=True):... | gpl-3.0 |
calico/basenji | bin/basenji_sat_plot2.py | 1 | 10706 | #!/usr/bin/env python
# Copyright 2017 Calico LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | apache-2.0 |
andrewnc/scikit-learn | doc/tutorial/text_analytics/solutions/exercise_02_sentiment.py | 254 | 2795 | """Build a sentiment analysis / polarity model
Sentiment analysis can be casted as a binary text classification problem,
that is fitting a linear classifier on features extracted from the text
of the user messages so as to guess wether the opinion of the author is
positive or negative.
In this examples we will use a ... | bsd-3-clause |
awacha/saxsfittool | src/saxsfittool/fitfunction/ellipsoid/ellipsoid.py | 1 | 7843 | import numpy as np
from matplotlib.axes import Axes
from matplotlib.patches import Ellipse
from .c_ellipsoid2 import AsymmetricEllipsoidalShell, EllipsoidalShellWithSizeDistribution
from .c_gauss_ellipsoid import I0Rgfromrho, rhofromI0Rg, F2GaussianEllipsoid
from ..core import FitFunction
class F2AsymmetricCoreShell... | bsd-3-clause |
aflaxman/scikit-learn | sklearn/model_selection/_search.py | 8 | 56392 | """
The :mod:`sklearn.model_selection._search` includes utilities to fine-tune the
parameters of an estimator.
"""
from __future__ import print_function
from __future__ import division
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>,
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# Andreas... | bsd-3-clause |
huobaowangxi/scikit-learn | sklearn/utils/tests/test_sparsefuncs.py | 57 | 13752 | import numpy as np
import scipy.sparse as sp
from scipy import linalg
from numpy.testing import assert_array_almost_equal, assert_array_equal
from sklearn.datasets import make_classification
from sklearn.utils.sparsefuncs import (mean_variance_axis,
inplace_column_scale,
... | bsd-3-clause |
arnabgho/sklearn-theano | sklearn_theano/feature_extraction/caffe/googlenet.py | 6 | 10608 | """Parser for bvlc caffe googlenet."""
# Authors: Michael Eickenberg
# Kyle Kastner
# License: BSD 3 Clause
from sklearn.externals import joblib
from ...datasets import get_dataset_dir, download
from .caffemodel import _parse_caffe_model, parse_caffe_model
import os
from ...utils import check_tensor, get_mini... | bsd-3-clause |
jlegendary/scikit-learn | examples/svm/plot_oneclass.py | 249 | 2302 | """
==========================================
One-class SVM with non-linear kernel (RBF)
==========================================
An example using a one-class SVM for novelty detection.
:ref:`One-class SVM <svm_outlier_detection>` is an unsupervised
algorithm that learns a decision function for novelty detection:
... | bsd-3-clause |
hargup/sympy | sympy/physics/quantum/tensorproduct.py | 64 | 13572 | """Abstract tensor product."""
from __future__ import print_function, division
from sympy import Expr, Add, Mul, Matrix, Pow, sympify
from sympy.core.compatibility import u, range
from sympy.core.trace import Tr
from sympy.printing.pretty.stringpict import prettyForm
from sympy.physics.quantum.qexpr import QuantumEr... | bsd-3-clause |
JeanKossaifi/scikit-learn | examples/covariance/plot_mahalanobis_distances.py | 348 | 6232 | r"""
================================================================
Robust covariance estimation and Mahalanobis distances relevance
================================================================
An example to show covariance estimation with the Mahalanobis
distances on Gaussian distributed data.
For Gaussian dis... | bsd-3-clause |
jmarcelogimenez/petroSym | petroSym/figureSampledLine.py | 1 | 9390 | # -*- coding: utf-8 -*-
"""
Created on Tue Aug 25 13:08:19 2015
@author: jgimenez
"""
from PyQt4 import QtGui, QtCore
from figureSampledLine_ui import figureSampledLineUI
from myNavigationToolbar import *
from temporalNavigationToolbar import *
from matplotlib.figure import Figure
from matplotlib.backends.backend_qt... | gpl-2.0 |
huzq/scikit-learn | sklearn/metrics/cluster/tests/test_bicluster.py | 19 | 1698 | """Testing for bicluster metrics module"""
import numpy as np
from sklearn.utils._testing import assert_almost_equal
from sklearn.metrics.cluster._bicluster import _jaccard
from sklearn.metrics import consensus_score
def test_jaccard():
a1 = np.array([True, True, False, False])
a2 = np.array([True, True, T... | bsd-3-clause |
huobaowangxi/scikit-learn | sklearn/linear_model/tests/test_ridge.py | 130 | 22974 | import numpy as np
import scipy.sparse as sp
from scipy import linalg
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_a... | bsd-3-clause |
SitiBanc/1061_NCTU_IOMDS | 1018/Homework4/HW4_2.py | 1 | 1454 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Oct 19 16:36:02 2017
@author: sitibanc
"""
from sklearn import datasets
import numpy as np
# K-NN Classifier
def knn(feature, target, K):
N = feature.shape[0] # N筆資料
L = np.zeros((N, 1)) # Label (data屬於哪一個class)
... | apache-2.0 |
dariocorral/panoanda | panoanda/tickers.py | 1 | 2954 | """
Created on Sat Sep 16 18:32:01 2017
@author: dariocorral
"""
import os
import oandapy
import pandas as pd
class Tickers(object):
"""
Basic info about tickers available for OANDA trading
"""
#oanda_api private attribute
_oanda_api = oandapy.API(environment = os.environ['ENV'],
... | mit |
Odingod/mne-python | mne/stats/tests/test_cluster_level.py | 8 | 20475 | import os
import os.path as op
import numpy as np
from numpy.testing import (assert_equal, assert_array_equal,
assert_array_almost_equal)
from nose.tools import assert_true, assert_raises
from scipy import sparse, linalg, stats
from mne.fixes import partial
import warnings
from mne.parallel i... | bsd-3-clause |
dkillick/iris | docs/iris/src/sphinxext/gen_gallery.py | 2 | 6305 | #
# (C) Copyright 2012 MATPLOTLIB (vn 1.2.0)
#
'''
Generate a thumbnail gallery of examples.
'''
from __future__ import (absolute_import, division, print_function)
from six.moves import (filter, input, map, range, zip) # noqa
import os
import glob
import re
import warnings
import matplotlib.image as image
from ... | lgpl-3.0 |
dragoon/kilogram | kilogram/entity_types/word2vec/model.py | 1 | 3920 | import numpy as np
from gensim.matutils import unitvec
from gensim.models import word2vec
# Import the built-in logging module and configure it so that Word2Vec
# creates nice output messages
import logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
def train_model(sou... | apache-2.0 |
hypergravity/bopy | bopy/spec/spec_quick_view.py | 1 | 14339 | # -*- coding: utf-8 -*-
"""
Author
------
Bo Zhang
Email
-----
bozhang@nao.cas.cn
Created on
----------
- Wed Feb 24 15:05:00 2016 spec_quick_view
Modifications
-------------
-
Aims
----
- method spec_quick_view: tool for quick view of spectra
"""
import numpy as np
import matplotlib.pyplot as plt
import as... | bsd-3-clause |
syl20bnr/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/image.py | 69 | 28764 | """
The image module supports basic image loading, rescaling and display
operations.
"""
from __future__ import division
import os, warnings
import numpy as np
from numpy import ma
from matplotlib import rcParams
from matplotlib import artist as martist
from matplotlib import colors as mcolors
from matplotlib import... | gpl-3.0 |
RobertABT/heightmap | build/matplotlib/examples/pylab_examples/subplots_demo.py | 9 | 2184 | """Examples illustrating the use of plt.subplots().
This function creates a figure and a grid of subplots with a single call, while
providing reasonable control over how the individual plots are created. For
very refined tuning of subplot creation, you can still use add_subplot()
directly on a new figure.
"""
import... | mit |
seunghwanl/APMAE4990 | webapp/__init__.py | 1 | 4985 | from flask import Flask, render_template, request, url_for, flash, redirect
from flask_bootstrap import Bootstrap
from sqlalchemy import *
from haversine import haversine
from sklearn.ensemble import RandomForestRegressor
from sklearn.externals import joblib
from forms import LatLongForm
import os
app = Flask(__n... | apache-2.0 |
rdipietro/tensorflow | tensorflow/contrib/factorization/python/ops/kmeans.py | 3 | 10875 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | apache-2.0 |
google-research/kubric | kubric/plotting.py | 1 | 5319 | # Copyright 2021 The Kubric Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in w... | apache-2.0 |
mjudsp/Tsallis | examples/datasets/plot_random_multilabel_dataset.py | 278 | 3402 | """
==============================================
Plot randomly generated multilabel dataset
==============================================
This illustrates the `datasets.make_multilabel_classification` dataset
generator. Each sample consists of counts of two features (up to 50 in
total), which are differently distri... | bsd-3-clause |
pprett/scikit-learn | examples/ensemble/plot_voting_decision_regions.py | 86 | 2386 | """
==================================================
Plot the decision boundaries of a VotingClassifier
==================================================
Plot the decision boundaries of a `VotingClassifier` for
two features of the Iris dataset.
Plot the class probabilities of the first sample in a toy dataset
pred... | bsd-3-clause |
tomlof/scikit-learn | examples/neighbors/plot_approximate_nearest_neighbors_hyperparameters.py | 102 | 5177 | """
=================================================
Hyper-parameters of Approximate Nearest Neighbors
=================================================
This example demonstrates the behaviour of the
accuracy of the nearest neighbor queries of Locality Sensitive Hashing
Forest as the number of candidates and the numb... | bsd-3-clause |
fengzhyuan/scikit-learn | sklearn/linear_model/tests/test_sparse_coordinate_descent.py | 244 | 9986 | import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_less
from sklearn.utils.testing import assert_true
from sklearn.utils.t... | bsd-3-clause |
jclosure/daw | DataAnalysisWeb/app/views.py | 1 | 2213 | """
Definition of views.
"""
from django.shortcuts import render
from django.http import HttpRequest
from django.template import RequestContext
from datetime import datetime
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import openpyxl
import xlrd
from django.utils.safestring import mark_safe... | mit |
TaylorOshan/pysal | pysal/weights/Contiguity.py | 5 | 14708 | from warnings import warn as Warn
from ..cg import asShape
from ..core.FileIO import FileIO
from .weights import W, WSP
from ._contW_binning import ContiguityWeightsPolygons
from ._contW_rtree import ContiguityWeights_rtree
from ._contW_lists import ContiguityWeightsLists
from .util import get_ids
WT_TYPE = {'rook': 2,... | bsd-3-clause |
franzpl/sweep | lin_sweep_kaiser_window_bandlimited_script6/lin_sweep_kaiser_window_bandlimited_script6.py | 2 | 2166 | #!/usr/bin/env python3
"""The influence of windowing of bandlimited lin. sweep signals when using a
Kaiser Window by fixing beta (=7) and fade_in (=0).
fstart = 100 Hz
fstop = 5000 Hz
Deconvolution: Windowed
"""
import sys
sys.path.append('..')
import measurement_chain
import plotting
import calculatio... | mit |
marcocaccin/scikit-learn | doc/conf.py | 210 | 8446 | # -*- coding: utf-8 -*-
#
# scikit-learn documentation build configuration file, created by
# sphinx-quickstart on Fri Jan 8 09:13:42 2010.
#
# This file is execfile()d with the current directory set to its containing
# dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
... | bsd-3-clause |
deepesch/scikit-learn | examples/svm/plot_rbf_parameters.py | 132 | 8096 | '''
==================
RBF SVM parameters
==================
This example illustrates the effect of the parameters ``gamma`` and ``C`` of
the Radial Basis Function (RBF) kernel SVM.
Intuitively, the ``gamma`` parameter defines how far the influence of a single
training example reaches, with low values meaning 'far' a... | bsd-3-clause |
tedunderwood/GenreProject | python/workshop/predictauthors.py | 1 | 4957 | import re, os
import csv
import random, pickle
import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
from bagofwords import WordVector, StandardizingVector
root = '/Users/tunder/Dropbox/GLNworkshop/USpoetry'
magazines = ['Bookman', 'Century', 'ContVerse', 'Crisis', 'Fugitive', 'Ha... | mit |
0asa/scikit-learn | examples/linear_model/plot_sgd_penalties.py | 249 | 1563 | """
==============
SGD: Penalties
==============
Plot the contours of the three penalties.
All of the above are supported by
:class:`sklearn.linear_model.stochastic_gradient`.
"""
from __future__ import division
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
def l1(xs):
return np.array([np.... | bsd-3-clause |
BjerknesClimateDataCentre/QuinCe | DataPreparation/SAMI/add_year.py | 2 | 1344 | """
Output files from the SAMI client software contain a decimal day of year
but no year. QuinCe requires a year.
Eventually I'll build suitable functionality into QuinCe, but until then
this utility can be used to add a Year column to SAMI output files.
This is in no way good Python code. Do not use it for reference... | gpl-3.0 |
enigmampc/catalyst | catalyst/pipeline/factors/crypto/technical.py | 1 | 24077 | """
Technical Analysis Factors
--------------------------
"""
from __future__ import division
from numbers import Number
from numpy import (
abs,
arange,
average,
clip,
diff,
dstack,
exp,
fmax,
full,
inf,
isnan,
log,
NINF,
sqrt,
sum as np_sum,
)
from numexpr ... | apache-2.0 |
willcode/gnuradio | gr-filter/examples/synth_to_chan.py | 6 | 3134 | #!/usr/bin/env python
#
# Copyright 2010,2012,2013 Free Software Foundation, Inc.
#
# This file is part of GNU Radio
#
# SPDX-License-Identifier: GPL-3.0-or-later
#
#
from gnuradio import gr
from gnuradio import blocks
from gnuradio import filter
import sys
import numpy
try:
from gnuradio import analog
except Imp... | gpl-3.0 |
jseabold/statsmodels | statsmodels/tsa/deterministic.py | 4 | 50951 | from statsmodels.compat.pandas import Appender, to_numpy
from abc import ABC, abstractmethod
import datetime as dt
from typing import Hashable, List, Optional, Sequence, Set, Tuple, Type, Union
import numpy as np
import pandas as pd
from scipy.linalg import qr
from statsmodels.iolib.summary import d_or_f
from statsm... | bsd-3-clause |
agrimaldi/metaseq | metaseq/arrayify.py | 3 | 4962 | """
Helper functions for converting genome-wide data into large NumPy arrays
"""
import os
import pandas
import pybedtools
import numpy as np
import _genomic_signal
class Binner(object):
def __init__(self, genome, windowsize, chrom=None, window_cache_dir=".",
npz_dir='.', metric='mean0'):
... | mit |
lcharleux/truss | doc/tests/test.py | 1 | 1991 | import truss
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib import patches
modulus = 210.e3 #Pa
rho = 2700. #kg/m**3
surface = .001 #m**2
yield_stress = 400 #Pa
from scipy import optimize
m = truss.core.Model()
A = m.add_node((0.,0.), label = "A")
B = ... | gpl-2.0 |
charanpald/tyre-hug | tyrehug/exp/svdbenchmark.py | 1 | 4877 | import time
import numpy
import sppy
import sppy.linalg
import matplotlib.pyplot as plt
import scipy.sparse
import os
from scipy.sparse.linalg import svds
from pypropack import svdp
from sparsesvd import sparsesvd
from sklearn.decomposition import TruncatedSVD
from sppy.linalg import GeneralLinearOperator
def time_re... | mit |
jor-/scipy | scipy/stats/kde.py | 5 | 21807 | #-------------------------------------------------------------------------------
#
# Define classes for (uni/multi)-variate kernel density estimation.
#
# Currently, only Gaussian kernels are implemented.
#
# Written by: Robert Kern
#
# Date: 2004-08-09
#
# Modified: 2005-02-10 by Robert Kern.
# Contr... | bsd-3-clause |
jjhelmus/scipy | scipy/signal/signaltools.py | 2 | 115724 | # Author: Travis Oliphant
# 1999 -- 2002
from __future__ import division, print_function, absolute_import
import warnings
import threading
import sys
import timeit
from . import sigtools, dlti
from ._upfirdn import upfirdn, _output_len
from scipy._lib.six import callable
from scipy._lib._version import NumpyVersion
... | bsd-3-clause |
Jimmy-Morzaria/scikit-learn | sklearn/decomposition/truncated_svd.py | 38 | 7697 | """Truncated SVD for sparse matrices, aka latent semantic analysis (LSA).
"""
# Author: Lars Buitinck <L.J.Buitinck@uva.nl>
# Olivier Grisel <olivier.grisel@ensta.org>
# Michael Becker <mike@beckerfuffle.com>
# License: 3-clause BSD.
import numpy as np
import scipy.sparse as sp
try:
from scipy.sp... | bsd-3-clause |
jmmease/pandas | pandas/tests/indexes/common.py | 2 | 37640 | # -*- coding: utf-8 -*-
import pytest
from pandas import compat
from pandas.compat import PY3
import numpy as np
from pandas import (Series, Index, Float64Index, Int64Index, UInt64Index,
RangeIndex, MultiIndex, CategoricalIndex, DatetimeIndex,
TimedeltaIndex, PeriodIndex, Int... | bsd-3-clause |
mortada/scipy | doc/source/tutorial/stats/plots/kde_plot3.py | 132 | 1229 | import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
np.random.seed(12456)
x1 = np.random.normal(size=200) # random data, normal distribution
xs = np.linspace(x1.min()-1, x1.max()+1, 200)
kde1 = stats.gaussian_kde(x1)
kde2 = stats.gaussian_kde(x1, bw_method='silverman')
fig = plt.figure(figsi... | bsd-3-clause |
RachitKansal/scikit-learn | examples/linear_model/plot_multi_task_lasso_support.py | 249 | 2211 | #!/usr/bin/env python
"""
=============================================
Joint feature selection with multi-task Lasso
=============================================
The multi-task lasso allows to fit multiple regression problems
jointly enforcing the selected features to be the same across
tasks. This example simulates... | bsd-3-clause |
viep/StockScraper | stockScraper.py | 1 | 3582 | from urllib2 import Request,urlopen,URLError
import sys
import mysql.connector
import time
import datetime
import os
import pandas as pd
import threading
from pandas.io import sql
from sqlalchemy import create_engine
def getPrice(tickers):
link = 'http://download.finance.yahoo.com/d/quotes.csv?'
arguments = 'f=aa2bb2... | mit |
466152112/scikit-learn | sklearn/datasets/base.py | 196 | 18554 | """
Base IO code for all datasets
"""
# Copyright (c) 2007 David Cournapeau <cournape@gmail.com>
# 2010 Fabian Pedregosa <fabian.pedregosa@inria.fr>
# 2010 Olivier Grisel <olivier.grisel@ensta.org>
# License: BSD 3 clause
import os
import csv
import shutil
from os import environ
from os.pa... | bsd-3-clause |
perryjohnson/biplaneblade | biplane_blade_lib/prep_stn16_mesh.py | 1 | 30758 | """Write initial TrueGrid files for one biplane blade station.
Usage
-----
start an IPython (qt)console with the pylab flag:
$ ipython qtconsole --pylab
or
$ ipython --pylab
Then, from the prompt, run this script:
|> %run biplane_blade_lib/prep_stnXX_mesh.py
or
|> import biplane_blade_lib/prep_stnXX_mesh
... | gpl-3.0 |
rubikloud/scikit-learn | sklearn/svm/classes.py | 6 | 40597 | import warnings
import numpy as np
from .base import _fit_liblinear, BaseSVC, BaseLibSVM
from ..base import BaseEstimator, RegressorMixin
from ..linear_model.base import LinearClassifierMixin, SparseCoefMixin, \
LinearModel
from ..feature_selection.from_model import _LearntSelectorMixin
from ..utils import check_X... | bsd-3-clause |
dominicelse/scipy | tools/refguide_check.py | 6 | 28914 | #!/usr/bin/env python
"""
refguide_check.py [OPTIONS] [-- ARGS]
Check for a Scipy submodule whether the objects in its __all__ dict
correspond to the objects included in the reference guide.
Example of usage::
$ python refguide_check.py optimize
Note that this is a helper script to be able to check if things ar... | bsd-3-clause |
nelson-liu/scikit-learn | doc/tutorial/text_analytics/skeletons/exercise_01_language_train_model.py | 103 | 2017 | """Build a language detector model
The goal of this exercise is to train a linear classifier on text features
that represent sequences of up to 3 consecutive characters so as to be
recognize natural languages by using the frequencies of short character
sequences as 'fingerprints'.
"""
# Author: Olivier Grisel <olivie... | bsd-3-clause |
georgid/sms-tools | lectures/3-Fourier-properties/plots-code/symmetry-real-even.py | 26 | 1150 | import matplotlib.pyplot as plt
import numpy as np
import sys
import math
from scipy.signal import triang
from scipy.fftpack import fft, fftshift
M = 127
N = 128
hM1 = int(math.floor((M+1)/2))
hM2 = int(math.floor(M/2))
x = triang(M)
fftbuffer = np.zeros(N)
fftbuffer[:hM1] = x[hM2:]
fftbuffer[N-hM2:] = x[:hM2]
X =... | agpl-3.0 |
fengzhyuan/scikit-learn | sklearn/utils/tests/test_random.py | 230 | 7344 | from __future__ import division
import numpy as np
import scipy.sparse as sp
from scipy.misc import comb as combinations
from numpy.testing import assert_array_almost_equal
from sklearn.utils.random import sample_without_replacement
from sklearn.utils.random import random_choice_csc
from sklearn.utils.testing import ... | bsd-3-clause |
fengzhyuan/scikit-learn | sklearn/covariance/robust_covariance.py | 198 | 29735 | """
Robust location and covariance estimators.
Here are implemented estimators that are resistant to outliers.
"""
# Author: Virgile Fritsch <virgile.fritsch@inria.fr>
#
# License: BSD 3 clause
import warnings
import numbers
import numpy as np
from scipy import linalg
from scipy.stats import chi2
from . import empir... | bsd-3-clause |
lxneng/incubator-airflow | airflow/hooks/presto_hook.py | 8 | 4438 | # -*- coding: utf-8 -*-
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
#... | apache-2.0 |
GoogleCloudPlatform/professional-services | tools/ml-auto-eda/ml_eda/preprocessing/preprocessors/bigquery/bq_preprocessor.py | 1 | 6974 | # Copyright 2019 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | apache-2.0 |
grochmal/capybara | src/proto/chopimg.py | 1 | 1388 | #!/usr/bin/env python
import os,sys
from PIL import Image
import numpy as np
import matplotlib
matplotlib.use('Agg')
from matplotlib import pyplot as plt
from matplotlib import cm
__doc__ = 'Usage: chopimg.py file n output'
def mark(ar, p):
yn, xn = ar.shape
xticks = [l*(xn/p) for l in range(1,p)]
yticks... | gpl-3.0 |
chrsrds/scikit-learn | sklearn/datasets/tests/test_lfw.py | 1 | 7469 | """This test for the LFW require medium-size data downloading and processing
If the data has not been already downloaded by running the examples,
the tests won't run (skipped).
If the test are run, the first execution will be long (typically a bit
more than a couple of minutes) but as the dataset loader is leveraging... | bsd-3-clause |
shisha101/grasp_SM | e2u/e2u_demo_setup/script/grasp.py | 1 | 84334 | #!/usr/bin/python
import rospy
import rospkg
import smach
import smach_ros
import tf
import sys
import copy
import std_srvs
import matplotlib.pyplot as plt
import numpy
import random
import pdb
from std_srvs.srv import Trigger, TriggerRequest, Empty
from geometry_msgs.msg import PoseStamped
from moveit_commander imp... | gpl-2.0 |
LumPenPacK/NetworkExtractionFromImages | osx_build/nefi2_osx_amd64_xcode_2015/site-packages/numpy/doc/creation.py | 118 | 5507 | """
==============
Array Creation
==============
Introduction
============
There are 5 general mechanisms for creating arrays:
1) Conversion from other Python structures (e.g., lists, tuples)
2) Intrinsic numpy array array creation objects (e.g., arange, ones, zeros,
etc.)
3) Reading arrays from disk, either from... | bsd-2-clause |
toobaz/pandas | pandas/tests/test_nanops.py | 2 | 43102 | from functools import partial
import warnings
import numpy as np
import pytest
import pandas.util._test_decorators as td
from pandas.core.dtypes.common import is_integer_dtype
import pandas as pd
from pandas import Series, isna
from pandas.core.arrays import DatetimeArray
import pandas.core.nanops as nanops
import ... | bsd-3-clause |
adamcandy/QGIS-Meshing | extras/contouring/bathymetry_metric.py | 3 | 26872 | #!/usr/bin/env python
##########################################################################
#
# Generation of boundary representation from arbitrary geophysical
# fields and initialisation for anisotropic, unstructured meshing.
#
# Copyright (C) 2011-2013 Dr Adam S. Candy, adam.candy@imperial.ac.uk
#
# ... | lgpl-2.1 |
anandha2017/udacity | nd101 Deep Learning Nanodegree Foundation/DockerImages/17_Weight_Initialisation/notebooks/weight-initialization/helper.py | 153 | 3649 | import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
def hist_dist(title, distribution_tensor, hist_range=(-4, 4)):
"""
Display histogram of a TF distribution
"""
with tf.Session() as sess:
values = sess.run(distribution_tensor)
plt.title(title)
plt.hist(values, ... | mit |
Barmaley-exe/scikit-learn | sklearn/linear_model/tests/test_sparse_coordinate_descent.py | 28 | 10014 | import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_less
from sklearn.utils.testing import assert_true
from sklearn.utils.t... | bsd-3-clause |
markneville/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/backends/backend_gdk.py | 69 | 15968 | from __future__ import division
import math
import os
import sys
import warnings
def fn_name(): return sys._getframe(1).f_code.co_name
import gobject
import gtk; gdk = gtk.gdk
import pango
pygtk_version_required = (2,2,0)
if gtk.pygtk_version < pygtk_version_required:
raise ImportError ("PyGTK %d.%d.%d is install... | agpl-3.0 |
SKIRT/PTS | magic/maps/dust/blackbody.py | 1 | 22556 | #!/usr/bin/env python
# -*- coding: utf8 -*-
# *****************************************************************
# ** PTS -- Python Toolkit for working with SKIRT **
# ** © Astronomical Observatory, Ghent University **
# *****************************************************************
##... | agpl-3.0 |
rrohan/scikit-learn | examples/ensemble/plot_adaboost_twoclass.py | 347 | 3268 | """
==================
Two-class AdaBoost
==================
This example fits an AdaBoosted decision stump on a non-linearly separable
classification dataset composed of two "Gaussian quantiles" clusters
(see :func:`sklearn.datasets.make_gaussian_quantiles`) and plots the decision
boundary and decision scores. The di... | bsd-3-clause |
CIFASIS/pylearn2 | pylearn2/scripts/papers/jia_huang_wkshp_11/evaluate.py | 44 | 3208 | from __future__ import print_function
from optparse import OptionParser
import warnings
try:
from sklearn.metrics import classification_report
except ImportError:
classification_report = None
warnings.warn("couldn't find sklearn.metrics.classification_report")
try:
from sklearn.metrics import confusion... | bsd-3-clause |
shuggiefisher/brain4k | brain4k/transforms/sklearn/__init__.py | 2 | 4927 | import logging
import numpy as np
from sklearn.cross_validation import train_test_split
from sklearn.naive_bayes import MultinomialNB
from brain4k.transforms import PipelineStage
class NaiveBayes(PipelineStage):
name = "org.scikit-learn.naive_bayes.MultinomialNB"
def train(self):
logging.debug(
... | apache-2.0 |
gfyoung/pandas | pandas/tests/groupby/test_value_counts.py | 5 | 4820 | """
these are systematically testing all of the args to value_counts
with different size combinations. This is to ensure stability of the sorting
and proper parameter handling
"""
from itertools import product
import numpy as np
import pytest
from pandas import (
Categorical,
CategoricalIndex,
DataFrame,... | bsd-3-clause |
johnbachman/indra | models/hello_indra.py | 6 | 1821 | import numpy as np
from pysb.integrate import Solver
from pysb.export import export
from matplotlib import pyplot as plt
from indra.sources import biopax
from indra.sources import bel, trips
from indra.util import plot_formatting as pf
from indra.assemblers.pysb import PysbAssembler
def plot_result(model, sol):
pf... | bsd-2-clause |
alexsavio/scikit-learn | sklearn/cross_validation.py | 11 | 69870 |
"""
The :mod:`sklearn.cross_validation` module includes utilities for cross-
validation and performance evaluation.
"""
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>,
# Gael Varoquaux <gael.varoquaux@normalesup.org>,
# Olivier Grisel <olivier.grisel@ensta.org>
# License: BSD 3 clause
fro... | bsd-3-clause |
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